14 research outputs found
Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedback
In most real-world recommender systems, the observed rating data are subject
to selection bias, and the data are thus missing-not-at-random. Developing a
method to facilitate the learning of a recommender with biased feedback is one
of the most challenging problems, as it is widely known that naive approaches
under selection bias often lead to suboptimal results. A well-established
solution for the problem is using propensity scoring techniques. The propensity
score is the probability of each data being observed, and unbiased performance
estimation is possible by weighting each data by the inverse of its propensity.
However, the performance of the propensity-based unbiased estimation approach
is often affected by choice of the propensity estimation model or the high
variance problem. To overcome these limitations, we propose a model-agnostic
meta-learning method inspired by the asymmetric tri-training framework for
unsupervised domain adaptation. The proposed method utilizes two predictors to
generate data with reliable pseudo-ratings and another predictor to make the
final predictions. In a theoretical analysis, a propensity-independent upper
bound of the true performance metric is derived, and it is demonstrated that
the proposed method can minimize this bound. We conduct comprehensive
experiments using public real-world datasets. The results suggest that the
previous propensity-based methods are largely affected by the choice of
propensity models and the variance problem caused by the inverse propensity
weighting. Moreover, we show that the proposed meta-learning method is robust
to these issues and can facilitate in developing effective recommendations from
biased explicit feedback.Comment: 43rd International ACM SIGIR Conference on Research and Development
in Information Retrieval (SIGIR '20
not-MIWAE: Deep Generative Modelling with Missing not at Random Data
When a missing process depends on the missing values themselves, it needs to
be explicitly modelled and taken into account while doing likelihood-based
inference. We present an approach for building and fitting deep latent variable
models (DLVMs) in cases where the missing process is dependent on the missing
data. Specifically, a deep neural network enables us to flexibly model the
conditional distribution of the missingness pattern given the data. This allows
for incorporating prior information about the type of missingness (e.g.
self-censoring) into the model. Our inference technique, based on
importance-weighted variational inference, involves maximising a lower bound of
the joint likelihood. Stochastic gradients of the bound are obtained by using
the reparameterisation trick both in latent space and data space. We show on
various kinds of data sets and missingness patterns that explicitly modelling
the missing process can be invaluable.Comment: Camera-ready version for ICLR 202
Unbiased Learning for the Causal Effect of Recommendation
Increasing users' positive interactions, such as purchases or clicks, is an
important objective of recommender systems. Recommenders typically aim to
select items that users will interact with. If the recommended items are
purchased, an increase in sales is expected. However, the items could have been
purchased even without recommendation. Thus, we want to recommend items that
results in purchases caused by recommendation. This can be formulated as a
ranking problem in terms of the causal effect. Despite its importance, this
problem has not been well explored in the related research. It is challenging
because the ground truth of causal effect is unobservable, and estimating the
causal effect is prone to the bias arising from currently deployed
recommenders. This paper proposes an unbiased learning framework for the causal
effect of recommendation. Based on the inverse propensity scoring technique,
the proposed framework first constructs unbiased estimators for ranking
metrics. Then, it conducts empirical risk minimization on the estimators with
propensity capping, which reduces variance under finite training samples. Based
on the framework, we develop an unbiased learning method for the causal effect
extension of a ranking metric. We theoretically analyze the unbiasedness of the
proposed method and empirically demonstrate that the proposed method
outperforms other biased learning methods in various settings.Comment: accepted at RecSys 2020, updated several experiment